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---
license: mit
task_categories:
- question-answering
- sentence-similarity
language:
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for "NLQuAD"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb](https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb)
### Dataset Summary
This is a simplified version of [vblagoje's](https://huggingface.co/vblagoje) [lfqa_support_docs](https://huggingface.co/datasets/vblagoje/lfqa_support_docs) and [lfqa](https://huggingface.co/datasets/vblagoje/lfqa) datasets.
It was generated by me to have a more straight forward way to train Seq2Seq models on context based long form question answering tasks.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{
"question": "Khashoggi murder: Body 'dissolved in acid'",
"answer": "2 November 2018",
"context": [
]
}
```
### Data Fields
The data fields are the same among all splits.
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `context`: a list feature containing `string` features.
### Data Splits
| name |train|test|validation|
|----------|----:|----:|---------:|
| |10259| 1280| 1280|
## Additional Information
### Licensing Information
This dataset is distributed under the MIT licence. |